This notebook is to keep track of results as they are generated on the final dataset for the Autoimmunity and Health project project. The project aims to quantify diagnostic journeys of chronically ill folk in Australia. Recruitment for both chronically ill participants as well as healthy controls will continue till June 2022. Accompanying code to generate images is in AD-report-code.R

Overview of participating cohort.

To date, 1366 people have participated in this project by taking the survey, 1183 chronically ill folk and 186 control cohort, recording 176 illnesses. Their distribution by resident state can be seen in the chart below, which is on par with the polulation density ratio between the states.

Responses by state

Distribution of responses by state

Distribution of responses by state

Age by gender distribution

Age, gender and cohort distribution

Gender breakdown between chronically ill and control cohorts by percentage:

Visualisaztion to compare age range between controls and chronic illness cohort shows that there is no statistical difference for age representation between HC and CI cohorts. How about by gender? Here, any identification other than Male and Female was grouped into Other due to the number of respondents with that identity.

Ethnicity

Income

Education level

Relationship status

Misdiagnsosis

There is a significant association between length to diagnosis and misdiagnosis based on Fisher’s exact test.

## 
##  Fisher's Exact Test for Count Data with simulated p-value (based on
##  2000 replicates)
## 
## data:  testx
## p-value = 0.0004998
## alternative hypothesis: two.sided

Autoimmune diorders and chronic illnesses represented in the cohort.

Primary illnesses reported.

In this cohort, 180 chronic illnesses have been reported by participants. Due to request from the chronic illness community, Hypermobility spectrum disorders were grouped together which include Ehlers-Danlos syndrome (EDS) (including variants such as hEDS, vEDS) as well as Hypermobility spectrum disorder (HSD) as diagnostic critreria changed in the last three years where hEDS would now be usually diagnosed as HSD. Further, vasculitis disorder were grouped together also as the underlying disease mechanism consists of vascular inflammation that presents in different tissues or types of blood vessels (i.e. Wegener’s granulomatosis, Takayasu’s Arteritis, Susac’s syndrome, Giant cell arteritis, Essential mixed cryoglobulinemia, Eosinophilic granulomatosis with polyangiitis (EGPA), cerebral vasculitis, general vasculitis, Leukocytoclastic vasculitis, Microscopic polyangiitis, Lymphocytic vasculitis, Henoch Schonlein purpura and Cutaneous small vessel vasculitis). Individually, these rare illnesses were so minorly represented as to not give any statistical power.

Another visual representation with more informative values are bar charts, as shown below. For this example, any illnesses with more than 10 entries are selected

Comorbidities found among participants.

Co-morbidities were reported in the control cohort also, however I think it is possible to not have to merge these as it is primarily asthma and mental illness.

Upset plot of top 10 represented ADs with misdiagnosis rate and employment status

Violing plot of y = Number of ADs, x = Length dx

SF36

Symptoms

Overview of symtptoms experienced

Symptom distribution between Chronically ill and control cohorts

## $`Chronically ill`
##                    symptom                                          
##     severity        bruise        chills          conc       fatigue
##         None  24.6%  (312)  46.5%  (589)  12.4%  (157)   3.5%   (44)
##    Very mild  13.4%  (170)  13.2%  (168)  12.5%  (158)   3.2%   (41)
##         Mild  15.0%  (190)  13.6%  (172)  19.1%  (242)   9.9%  (126)
##     Moderate  22.2%  (282)   9.1%  (115)  26.2%  (332)  29.9%  (379)
##       Severe  10.7%  (136)   3.4%   (43)  13.0%  (165)  30.4%  (385)
##  Very severe   2.9%   (37)   0.6%    (8)   6.0%   (76)  13.5%  (171)
##         <NA>  11.1%  (141)  13.6%  (173)  10.9%  (138)   9.6%  (122)
##        Total 100.0% (1268) 100.0% (1268) 100.0% (1268) 100.0% (1268)
##                                                                       
##          fever        glands          hair         joint        memory
##   56.9%  (722)  41.8%  (530)  38.8%  (492)  15.4%  (195)  21.6%  (274)
##   10.6%  (135)  16.0%  (203)  12.9%  (163)   9.0%  (114)  14.5%  (184)
##   10.9%  (138)  13.8%  (175)  15.3%  (194)  14.1%  (179)  21.7%  (275)
##    5.9%   (75)  11.1%  (141)  14.2%  (180)  27.9%  (354)  19.7%  (250)
##    2.1%   (26)   3.5%   (45)   5.4%   (68)  17.0%  (216)   7.4%   (94)
##    0.8%   (10)   0.7%    (9)   1.8%   (23)   5.9%   (75)   3.2%   (41)
##   12.8%  (162)  13.0%  (165)  11.7%  (148)  10.6%  (135)  11.8%  (150)
##  100.0% (1268) 100.0% (1268) 100.0% (1268) 100.0% (1268) 100.0% (1268)
##                                                                       
##           pain          skin         skin2       stomach          weak
##   11.7%  (148)  21.1%  (267)  35.5%  (450)  12.1%  (153)  13.7%  (174)
##    9.6%  (122)  13.2%  (168)  11.5%  (146)   8.5%  (108)  10.6%  (134)
##   10.2%  (129)  21.3%  (270)  14.6%  (185)  16.1%  (204)  16.5%  (209)
##   26.3%  (334)  23.5%  (298)  16.0%  (203)  29.7%  (376)  25.2%  (319)
##   21.8%  (276)   7.0%   (89)   6.7%   (85)  16.2%  (205)  14.4%  (183)
##   10.0%  (127)   2.7%   (34)   2.8%   (36)   7.3%   (93)   8.3%  (105)
##   10.4%  (132)  11.2%  (142)  12.9%  (163)  10.2%  (129)  11.4%  (144)
##  100.0% (1268) 100.0% (1268) 100.0% (1268) 100.0% (1268) 100.0% (1268)
## 
## $`Control group`
##                   symptom                                                    
##     severity       bruise       chills         conc      fatigue        fever
##         None  48.5%  (49)  77.2%  (78)  39.6%  (40)  24.8%  (25)  81.2%  (82)
##    Very mild  12.9%  (13)   4.0%   (4)  22.8%  (23)  18.8%  (19)   1.0%   (1)
##         Mild  11.9%  (12)   3.0%   (3)   7.9%   (8)  15.8%  (16)   3.0%   (3)
##     Moderate   6.9%   (7)   1.0%   (1)  11.9%  (12)  20.8%  (21)   0.0%   (0)
##       Severe   3.0%   (3)   0.0%   (0)   3.0%   (3)   3.0%   (3)   0.0%   (0)
##  Very severe   1.0%   (1)   0.0%   (0)   1.0%   (1)   2.0%   (2)   0.0%   (0)
##         <NA>  15.8%  (16)  14.9%  (15)  13.9%  (14)  14.9%  (15)  14.9%  (15)
##        Total 100.0% (101) 100.0% (101) 100.0% (101) 100.0% (101) 100.0% (101)
##                                                                               
##        glands         hair        joint       memory         pain         skin
##   75.2%  (76)  63.4%  (64)  49.5%  (50)  56.4%  (57)  48.5%  (49)  47.5%  (48)
##    5.9%   (6)   8.9%   (9)  10.9%  (11)  14.9%  (15)  14.9%  (15)   9.9%  (10)
##    4.0%   (4)   6.9%   (7)  10.9%  (11)   7.9%   (8)   7.9%   (8)  16.8%  (17)
##    0.0%   (0)   4.0%   (4)  10.9%  (11)   5.9%   (6)  10.9%  (11)  10.9%  (11)
##    0.0%   (0)   1.0%   (1)   3.0%   (3)   1.0%   (1)   3.0%   (3)   1.0%   (1)
##    0.0%   (0)   2.0%   (2)   1.0%   (1)   1.0%   (1)   1.0%   (1)   2.0%   (2)
##   14.9%  (15)  13.9%  (14)  13.9%  (14)  12.9%  (13)  13.9%  (14)  11.9%  (12)
##  100.0% (101) 100.0% (101) 100.0% (101) 100.0% (101) 100.0% (101) 100.0% (101)
##                                        
##         skin2      stomach         weak
##   61.4%  (62)  39.6%  (40)  56.4%  (57)
##    6.9%   (7)  11.9%  (12)  16.8%  (17)
##    7.9%   (8)  11.9%  (12)   5.0%   (5)
##    5.9%   (6)  15.8%  (16)   4.0%   (4)
##    1.0%   (1)   5.0%   (5)   2.0%   (2)
##    1.0%   (1)   2.0%   (2)   1.0%   (1)
##   15.8%  (16)  13.9%  (14)  14.9%  (15)
##  100.0% (101) 100.0% (101) 100.0% (101)

Symptoms for top 10 most represented illnesses

image image image image image image image image image image #### Individual symptoms for controls vs. chronic cohorts

Heatmaps and networks of symptoms

Overall symptoms

Gender difference in symptoms

Males

### Females ### Other genders ### Differential network male and female

Cohort difference in symptoms

Controls

### Chronic

Differential network control and chronic cohorts

Misdiagnosis and diagnosis

Correct diagnosis

### Misdagnosis

Differential network between correct and misdiagnosis